A robust algorithm for subspace clustering of high-dimensional data. | [Information technology Journal • 2007]

Author(s):

1. Hongfang Zhou: School of Electronics and Information Engineering, Xi’an Jiaotong University, Xian, Shaanxi, China

2. Boqin Feng: School of Electronics and Information Engineering, Xi’an Jiaotong University, Xian, Shaanxi, China

3. Yue Hui: School of Electronics and Information Engineering, Xi’an Jiaotong University, Xian, Shaanxi, China

4. L. V. Lintao: School of Computer Science and Engineering, Xi’an Jiaotogn University, of Technology, Xi’an, Shaanxi, China

Abstract:

Subspace clustering has been studied extensively and widely since traditional algorithms are ineffective in high-dimensional data spaces. Firstly, they were sensitive to noises, which are inevitable in high-dimensional data spaces; secondly, they were too severely dependent on some distance metrics, which cannot act as virtual indicators as in high-dimensional data spaces; thirdly, they often use a global threshold, but different groups of features behave differently in various dimensional subspaces. Accordingly, traditional clustering algorithms are not suitable in high-dimensional spaces. On the analysis of the advantages and disadvantages inherent to the traditional clustering algorithm, we propose a robust algorithm JPA (Joining-Pruning Algorithm). Our algorithm is based on an efficient two-phase architecture. The experiments show that our algorithm achieves a significant gain of runtime and quality in comparison to nowadays subspace clustering algorithms.

Page(s): 255-258

DOI: DOI not available

Published: Journal: Information technology Journal, Volume: 6, Issue: 2, Year: 2007

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